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UW Optimization Home Page



CS 726: Nonlinear Optimization I
Fall 2015 (also ISyE, Stat, Math)
Instructor 

Michael C. Ferris
 Office: 4381 CS&S
 Telephone: 2624281
 Email: I will not respond to questions about class material via
email. We will use Piazza for this.
 Office Hours: 12:00  1:00 Mondays, 11:00  12:00 Wednesdays

Lecture 
 9:55  10:45 MWF, 2540 Eng Hall

Mailing list  compsci7261f15@lists.wisc.edu

Piazza: questions and answers 
Getting Started
Piazza is an online tool for asking and answering questions. Piazza is available to you anywhere you have access to the internet. It is delivered and supported by a company called Piazza. Their home page is:
http://www.piazza.com
Registration
Logging In to Piazza
Once you're logged in, you can stay logged in on that computer. You will need to relogin if you check Piazza from another computer.

Course URL  http://www.cs.wisc.edu/~cs7261

General Course Information (http://www.cs.wisc.edu/~ferris/cs726.html)
Course Outline
Theory and algorithms for nonlinear optimization, focusing on unconstrained optimization. Linesearch and trustregion methods; quasiNewton methods; conjugategradient and limitedmemory methods for largescale problems; derivativefree optimization; algorithms for leastsquares problems and nonlinear equations; gradient projection algorithms for boundconstrained problems; and simple penalty methods for nonlinearly constrained optimization.
 Continuous optimization paradigms
 Representative applications
 Mathematical background, including convex sets and functions
 Unconstrained optimization: Theory and algorithms
 Optimality conditions
 Gradient methods and Newton's method
 Line search methods
 QuasiNewton methods
 DerivativeFree optimization
 Modelbased methods
 Patternsearch methods
 Largescale unconstrained optimization:
 Conjugate gradient methods (linear and nonlinear)
 Limitedmemory quasiNewton methods
 Duality
 Nonlinear equations and leastsquares problems
 Optimization with bound constraints:
 Projections and gradient projection algorithms
 Enhancements of gradient projection using secondorder information and quasiNewton techniques.
 Constrained nonlinear programming algorithms
 Constraint elimination
 Penalty methods
Required Text
 I will use a set of notes specially prepared for this course.
They will cover the first part of the Nocedal and Wright book.
 Numerical Optimization, J. Nocedal and S.J. Wright,
Springer Series in Operations Research, SpringerVerlag, New York,
2006 (2nd edition).
Other References:

Convex Optimization, S. Boyd and L. Vandenberghe,
Cambridge University Press, UK 2004.
 Nonlinear Optimization, Andrzej Ruszczynski,
Princeton University Press, NJ 2006.
 Nonlinear Programming, 2nd Edition, Dimitri Bertsekas,
Athena Scientific, Belmont, MA 1999.
 Practical Methods of Optimization, 2nd Edition, R. Fletcher,
Wiley, 1987.
 Practical Optimization, P. Gill, W. Murray and M. Wright,
Academic Press, 1981.
 Nonlinear Programming Theory and Algorithms , M. S. Bazaraa, H. D. Sherali and C. M. Shetty,
Second Edition, Wiley, New York 2006.

The MATLAB PRIMER (Third Edition): An introduction to the
basic commands and utilities that you may need in Matlab.
Scribing
Programming Assignments and Homeworks
All assignments need to be written up entirely separately.
You may discuss the problems informally with others in the class, but the
discussions must not include code or explicit solutions to any of
the problems.
 N Assignments total.
Most homeworks will be handed in either in hard copy or using the drop box facility of Learn@UW. The drop box menu is found in the top menu bar once you have logged into the system.
Most of the assignments will
require the use of MATLAB, which will also be used extensively in the
lectures.
No homework or project accepted in mailbox of instructor.
Further details will be provided when the assignments are passed out.
Homework due at beginning of class one week after assigned unless otherwise noted.

Examinations are closed book, with the exception that 1
handwritten sheet (standard size paper) can be brought in to the examination.
 Midterm Examination: Monday October 12 at 7:15  9:15pm in EH 2535.
 Final Examination 
Monday, December 21 at 10:05 am  12:05 pm
in 1221 CS.
Previous exams (from 730) are given below, relevant questions are:
2003 (Q1), 2004 (Q2), 2005 (Q1)

Final Exam 2003

Final Exam 2004

Final Exam 2005

Final Exam 2009

Final Exam 2011
 Prereq: Familiarity with basic analysis (e.g. Math 521) and either Math 443 or 320, or consent of instructor
Grading
CS Department Computing Information
Miscellaneous
This page was updated September 2, 2015.


